Overview

Dataset statistics

Number of variables20
Number of observations76321
Missing cells523298
Missing cells (%)34.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 MiB
Average record size in memory168.0 B

Variable types

Categorical2
Numeric17
Unsupported1

Alerts

TOT-RF has constant value ""Constant
Date has a high cardinality: 76320 distinct valuesHigh cardinality
PM2.5 is highly overall correlated with PM10 and 2 other fieldsHigh correlation
PM10 is highly overall correlated with PM2.5 and 2 other fieldsHigh correlation
NO is highly overall correlated with NOxHigh correlation
NO2 is highly overall correlated with PM2.5High correlation
NOx is highly overall correlated with NOHigh correlation
RH is highly overall correlated with PM10 and 1 other fieldsHigh correlation
BP is highly overall correlated with PM2.5 and 2 other fieldsHigh correlation
PM2.5 has 10908 (14.3%) missing valuesMissing
PM10 has 24865 (32.6%) missing valuesMissing
NO has 22216 (29.1%) missing valuesMissing
NO2 has 29800 (39.0%) missing valuesMissing
NOx has 18772 (24.6%) missing valuesMissing
NH3 has 32291 (42.3%) missing valuesMissing
SO2 has 23913 (31.3%) missing valuesMissing
CO has 22559 (29.6%) missing valuesMissing
Ozone has 23270 (30.5%) missing valuesMissing
Benzene has 13945 (18.3%) missing valuesMissing
Toluene has 76321 (100.0%) missing valuesMissing
Eth-Benzene has 39409 (51.6%) missing valuesMissing
MP-Xylene has 44953 (58.9%) missing valuesMissing
RH has 20376 (26.7%) missing valuesMissing
WS has 20383 (26.7%) missing valuesMissing
WD has 20348 (26.7%) missing valuesMissing
BP has 58514 (76.7%) missing valuesMissing
AT has 20455 (26.8%) missing valuesMissing
Ozone is highly skewed (γ1 = 20.97650593)Skewed
Date is uniformly distributedUniform
Toluene is an unsupported type, check if it needs cleaning or further analysisUnsupported
NOx has 5169 (6.8%) zerosZeros
CO has 1053 (1.4%) zerosZeros
Benzene has 35154 (46.1%) zerosZeros

Reproduction

Analysis started2023-03-20 16:59:59.758633
Analysis finished2023-03-20 17:00:53.053485
Duration53.29 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct76320
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2023-07-03 00:00:00
 
2
2022-06-15 01:30:00
 
1
2022-06-15 01:00:00
 
1
2022-06-15 00:45:00
 
1
2022-06-15 00:30:00
 
1
Other values (76315)
76315 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1450099
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76319 ?
Unique (%)> 99.9%

Sample

1st row2021-01-01 00:15:00
2nd row2021-01-01 00:30:00
3rd row2021-01-01 00:45:00
4th row2021-01-01 01:00:00
5th row2021-01-01 01:15:00

Common Values

ValueCountFrequency (%)
2023-07-03 00:00:00 2
 
< 0.1%
2022-06-15 01:30:00 1
 
< 0.1%
2022-06-15 01:00:00 1
 
< 0.1%
2022-06-15 00:45:00 1
 
< 0.1%
2022-06-15 00:30:00 1
 
< 0.1%
2022-06-15 00:15:00 1
 
< 0.1%
2022-06-15 00:00:00 1
 
< 0.1%
2022-06-14 23:45:00 1
 
< 0.1%
2022-06-14 23:30:00 1
 
< 0.1%
2022-06-14 23:15:00 1
 
< 0.1%
Other values (76310) 76310
> 99.9%

Length

2023-03-20T22:30:53.217429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 796
 
0.5%
11:15:00 795
 
0.5%
20:00:00 795
 
0.5%
17:15:00 795
 
0.5%
17:45:00 795
 
0.5%
18:00:00 795
 
0.5%
18:15:00 795
 
0.5%
16:45:00 795
 
0.5%
18:30:00 795
 
0.5%
19:00:00 795
 
0.5%
Other values (882) 144691
94.8%

Most occurring characters

ValueCountFrequency (%)
0 422397
29.1%
2 256214
17.7%
1 161793
 
11.2%
- 152642
 
10.5%
: 152642
 
10.5%
76321
 
5.3%
5 58056
 
4.0%
3 52624
 
3.6%
4 38784
 
2.7%
7 19802
 
1.4%
Other values (3) 58824
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1068494
73.7%
Dash Punctuation 152642
 
10.5%
Other Punctuation 152642
 
10.5%
Space Separator 76321
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 422397
39.5%
2 256214
24.0%
1 161793
 
15.1%
5 58056
 
5.4%
3 52624
 
4.9%
4 38784
 
3.6%
7 19802
 
1.9%
8 19800
 
1.9%
6 19704
 
1.8%
9 19320
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 152642
100.0%
Other Punctuation
ValueCountFrequency (%)
: 152642
100.0%
Space Separator
ValueCountFrequency (%)
76321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1450099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 422397
29.1%
2 256214
17.7%
1 161793
 
11.2%
- 152642
 
10.5%
: 152642
 
10.5%
76321
 
5.3%
5 58056
 
4.0%
3 52624
 
3.6%
4 38784
 
2.7%
7 19802
 
1.4%
Other values (3) 58824
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1450099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 422397
29.1%
2 256214
17.7%
1 161793
 
11.2%
- 152642
 
10.5%
: 152642
 
10.5%
76321
 
5.3%
5 58056
 
4.0%
3 52624
 
3.6%
4 38784
 
2.7%
7 19802
 
1.4%
Other values (3) 58824
 
4.1%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6908
Distinct (%)10.6%
Missing10908
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean47.998834
Minimum0.02
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:53.503393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile7.37
Q118
median39
Q370
95-th percentile117
Maximum340
Range339.98
Interquartile range (IQR)52

Descriptive statistics

Standard deviation36.327932
Coefficient of variation (CV)0.75685031
Kurtosis2.214152
Mean47.998834
Median Absolute Deviation (MAD)24
Skewness1.1886124
Sum3139747.7
Variance1319.7187
MonotonicityNot monotonic
2023-03-20T22:30:53.717803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 1156
 
1.5%
11 1119
 
1.5%
12 992
 
1.3%
10 965
 
1.3%
9 961
 
1.3%
14 951
 
1.2%
13 944
 
1.2%
15 940
 
1.2%
16 916
 
1.2%
8 880
 
1.2%
Other values (6898) 55589
72.8%
(Missing) 10908
 
14.3%
ValueCountFrequency (%)
0.02 1
< 0.1%
0.09 1
< 0.1%
0.13 1
< 0.1%
0.16 1
< 0.1%
0.18 1
< 0.1%
0.22 1
< 0.1%
0.27 1
< 0.1%
0.4 1
< 0.1%
0.47 2
< 0.1%
0.5 1
< 0.1%
ValueCountFrequency (%)
340 1
 
< 0.1%
332 1
 
< 0.1%
328 3
< 0.1%
323 2
 
< 0.1%
315 6
< 0.1%
314.58 1
 
< 0.1%
314 3
< 0.1%
311.56 1
 
< 0.1%
308.44 1
 
< 0.1%
306.33 1
 
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8179
Distinct (%)15.9%
Missing24865
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean102.97507
Minimum4
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:53.956256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile26
Q152
median87
Q3141
95-th percentile221.5675
Maximum985
Range981
Interquartile range (IQR)89

Descriptive statistics

Standard deviation67.31922
Coefficient of variation (CV)0.65374289
Kurtosis14.923985
Mean102.97507
Median Absolute Deviation (MAD)41.1
Skewness2.0972745
Sum5298685.2
Variance4531.8774
MonotonicityNot monotonic
2023-03-20T22:30:54.189183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 430
 
0.6%
59 429
 
0.6%
48 392
 
0.5%
53 377
 
0.5%
41 374
 
0.5%
44 367
 
0.5%
52 354
 
0.5%
66 343
 
0.4%
33 338
 
0.4%
46 335
 
0.4%
Other values (8169) 47717
62.5%
(Missing) 24865
32.6%
ValueCountFrequency (%)
4 3
 
< 0.1%
4.13 1
 
< 0.1%
5 2
 
< 0.1%
5.33 1
 
< 0.1%
5.8 1
 
< 0.1%
5.95 1
 
< 0.1%
6 4
 
< 0.1%
7 13
< 0.1%
7.06 1
 
< 0.1%
7.4 2
 
< 0.1%
ValueCountFrequency (%)
985 20
< 0.1%
931.84 1
 
< 0.1%
801 3
 
< 0.1%
798.8 1
 
< 0.1%
755.1 1
 
< 0.1%
648 1
 
< 0.1%
632.76 1
 
< 0.1%
610 2
 
< 0.1%
599 1
 
< 0.1%
593 3
 
< 0.1%

NO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16926
Distinct (%)31.3%
Missing22216
Missing (%)29.1%
Infinite0
Infinite (%)0.0%
Mean59.075482
Minimum0.01
Maximum499.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:54.388044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.75
Q18.65
median19.46
Q385.38
95-th percentile238.966
Maximum499.89
Range499.88
Interquartile range (IQR)76.73

Descriptive statistics

Standard deviation81.152845
Coefficient of variation (CV)1.3737145
Kurtosis4.9649654
Mean59.075482
Median Absolute Deviation (MAD)14.4
Skewness2.1246649
Sum3196279
Variance6585.7842
MonotonicityNot monotonic
2023-03-20T22:30:54.732418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 45
 
0.1%
7.94 33
 
< 0.1%
0.02 33
 
< 0.1%
9.66 33
 
< 0.1%
0.04 32
 
< 0.1%
7.4 31
 
< 0.1%
6.93 30
 
< 0.1%
6.33 30
 
< 0.1%
6.57 30
 
< 0.1%
6.17 30
 
< 0.1%
Other values (16916) 53778
70.5%
(Missing) 22216
29.1%
ValueCountFrequency (%)
0.01 45
0.1%
0.02 33
< 0.1%
0.03 21
< 0.1%
0.04 32
< 0.1%
0.05 28
< 0.1%
0.06 24
< 0.1%
0.07 15
 
< 0.1%
0.08 20
< 0.1%
0.09 12
 
< 0.1%
0.1 20
< 0.1%
ValueCountFrequency (%)
499.89 1
< 0.1%
499.51 1
< 0.1%
499.4 1
< 0.1%
498.56 1
< 0.1%
498.47 1
< 0.1%
498.34 1
< 0.1%
498.02 1
< 0.1%
497.99 1
< 0.1%
497.45 1
< 0.1%
497.36 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6541
Distinct (%)14.1%
Missing29800
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean16.836007
Minimum0.01
Maximum221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:54.955842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.42
Q14.58
median12.04
Q323.23
95-th percentile49.73
Maximum221
Range220.99
Interquartile range (IQR)18.65

Descriptive statistics

Standard deviation16.966657
Coefficient of variation (CV)1.0077601
Kurtosis7.4466112
Mean16.836007
Median Absolute Deviation (MAD)8.58
Skewness2.1002789
Sum783227.9
Variance287.86745
MonotonicityNot monotonic
2023-03-20T22:30:55.192173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 214
 
0.3%
0.02 172
 
0.2%
0.03 131
 
0.2%
0.04 114
 
0.1%
0.05 83
 
0.1%
0.08 82
 
0.1%
0.07 79
 
0.1%
0.06 76
 
0.1%
0.14 62
 
0.1%
0.1 59
 
0.1%
Other values (6531) 45449
59.5%
(Missing) 29800
39.0%
ValueCountFrequency (%)
0.01 214
0.3%
0.02 172
0.2%
0.03 131
0.2%
0.04 114
0.1%
0.05 83
 
0.1%
0.06 76
 
0.1%
0.07 79
 
0.1%
0.08 82
 
0.1%
0.09 55
 
0.1%
0.1 59
 
0.1%
ValueCountFrequency (%)
221 1
< 0.1%
186.68 1
< 0.1%
185.48 1
< 0.1%
185.36 1
< 0.1%
182.84 1
< 0.1%
181.03 1
< 0.1%
177.87 1
< 0.1%
176.54 1
< 0.1%
164.24 1
< 0.1%
161.83 1
< 0.1%

NOx
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18631
Distinct (%)32.4%
Missing18772
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean67.977576
Minimum0
Maximum499.92
Zeros5169
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:55.402449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.45
median37.55
Q391.8
95-th percentile236.022
Maximum499.92
Range499.92
Interquartile range (IQR)73.35

Descriptive statistics

Standard deviation78.044491
Coefficient of variation (CV)1.1480917
Kurtosis4.9563468
Mean67.977576
Median Absolute Deviation (MAD)25.72
Skewness2.0427667
Sum3912041.5
Variance6090.9426
MonotonicityNot monotonic
2023-03-20T22:30:55.638866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5169
 
6.8%
0.02 38
 
< 0.1%
0.01 28
 
< 0.1%
0.03 25
 
< 0.1%
0.04 20
 
< 0.1%
0.08 18
 
< 0.1%
11.7 18
 
< 0.1%
22.57 18
 
< 0.1%
23.74 18
 
< 0.1%
25.9 18
 
< 0.1%
Other values (18621) 52179
68.4%
(Missing) 18772
 
24.6%
ValueCountFrequency (%)
0 5169
6.8%
0.01 28
 
< 0.1%
0.02 38
 
< 0.1%
0.03 25
 
< 0.1%
0.04 20
 
< 0.1%
0.05 13
 
< 0.1%
0.06 18
 
< 0.1%
0.07 15
 
< 0.1%
0.08 18
 
< 0.1%
0.09 13
 
< 0.1%
ValueCountFrequency (%)
499.92 1
< 0.1%
499.52 1
< 0.1%
499.48 1
< 0.1%
499.17 1
< 0.1%
498.99 1
< 0.1%
498.38 1
< 0.1%
498.06 1
< 0.1%
497.7 1
< 0.1%
497.67 1
< 0.1%
497.59 1
< 0.1%

NH3
Real number (ℝ)

Distinct8106
Distinct (%)18.4%
Missing32291
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean32.27553
Minimum0.01
Maximum498.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:55.875676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.52
Q15.53
median11.46
Q320.29
95-th percentile181.9155
Maximum498.59
Range498.58
Interquartile range (IQR)14.76

Descriptive statistics

Standard deviation61.639563
Coefficient of variation (CV)1.9097925
Kurtosis10.229047
Mean32.27553
Median Absolute Deviation (MAD)6.79
Skewness3.0705354
Sum1421091.6
Variance3799.4358
MonotonicityNot monotonic
2023-03-20T22:30:56.220788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 171
 
0.2%
0.02 110
 
0.1%
0.03 86
 
0.1%
0.04 76
 
0.1%
0.08 61
 
0.1%
0.06 56
 
0.1%
0.09 56
 
0.1%
0.15 55
 
0.1%
0.17 54
 
0.1%
0.05 53
 
0.1%
Other values (8096) 43252
56.7%
(Missing) 32291
42.3%
ValueCountFrequency (%)
0.01 171
0.2%
0.02 110
0.1%
0.03 86
0.1%
0.04 76
0.1%
0.05 53
 
0.1%
0.06 56
 
0.1%
0.07 51
 
0.1%
0.08 61
 
0.1%
0.09 56
 
0.1%
0.1 47
 
0.1%
ValueCountFrequency (%)
498.59 1
< 0.1%
495.84 1
< 0.1%
495.68 1
< 0.1%
493.19 1
< 0.1%
490.66 1
< 0.1%
488.9 1
< 0.1%
487.16 1
< 0.1%
486.82 1
< 0.1%
486.25 1
< 0.1%
478.96 1
< 0.1%

SO2
Real number (ℝ)

Distinct5156
Distinct (%)9.8%
Missing23913
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean19.560937
Minimum0.01
Maximum182.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:56.456615image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.83
Q111.01
median19.17
Q325.68
95-th percentile37.65
Maximum182.56
Range182.55
Interquartile range (IQR)14.67

Descriptive statistics

Standard deviation11.458251
Coefficient of variation (CV)0.58577209
Kurtosis11.381569
Mean19.560937
Median Absolute Deviation (MAD)7.3
Skewness1.7173321
Sum1025149.6
Variance131.29151
MonotonicityNot monotonic
2023-03-20T22:30:56.700327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.65 40
 
0.1%
23.77 38
 
< 0.1%
24.86 37
 
< 0.1%
24.04 34
 
< 0.1%
23.81 34
 
< 0.1%
23.68 34
 
< 0.1%
5.59 33
 
< 0.1%
22.92 33
 
< 0.1%
22.55 33
 
< 0.1%
20.33 32
 
< 0.1%
Other values (5146) 52060
68.2%
(Missing) 23913
31.3%
ValueCountFrequency (%)
0.01 26
< 0.1%
0.02 21
< 0.1%
0.03 18
< 0.1%
0.04 18
< 0.1%
0.05 20
< 0.1%
0.06 10
 
< 0.1%
0.07 9
 
< 0.1%
0.08 7
 
< 0.1%
0.09 5
 
< 0.1%
0.1 5
 
< 0.1%
ValueCountFrequency (%)
182.56 1
< 0.1%
165.35 1
< 0.1%
160.08 1
< 0.1%
159.08 1
< 0.1%
157.97 1
< 0.1%
152.49 1
< 0.1%
147.99 1
< 0.1%
146.54 1
< 0.1%
145.97 1
< 0.1%
144.87 1
< 0.1%

CO
Real number (ℝ)

MISSING  ZEROS 

Distinct318
Distinct (%)0.6%
Missing22559
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean0.92233864
Minimum0
Maximum6.55
Zeros1053
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:56.963918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.47
median0.97
Q31.32
95-th percentile1.9
Maximum6.55
Range6.55
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation0.58174261
Coefficient of variation (CV)0.63072562
Kurtosis-0.1353309
Mean0.92233864
Median Absolute Deviation (MAD)0.43
Skewness0.36926827
Sum49586.77
Variance0.33842446
MonotonicityNot monotonic
2023-03-20T22:30:57.201404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1053
 
1.4%
1.03 895
 
1.2%
0.65 766
 
1.0%
1.02 748
 
1.0%
1.04 711
 
0.9%
0.66 690
 
0.9%
0.68 634
 
0.8%
0.64 631
 
0.8%
0.11 605
 
0.8%
0.67 600
 
0.8%
Other values (308) 46429
60.8%
(Missing) 22559
29.6%
ValueCountFrequency (%)
0 1053
1.4%
0.01 351
 
0.5%
0.02 226
 
0.3%
0.03 266
 
0.3%
0.04 334
 
0.4%
0.05 379
 
0.5%
0.06 353
 
0.5%
0.07 382
 
0.5%
0.08 378
 
0.5%
0.09 391
 
0.5%
ValueCountFrequency (%)
6.55 1
< 0.1%
5.11 1
< 0.1%
4.02 1
< 0.1%
3.47 1
< 0.1%
3.46 1
< 0.1%
3.43 1
< 0.1%
3.38 1
< 0.1%
3.36 1
< 0.1%
3.35 1
< 0.1%
3.32 1
< 0.1%

Ozone
Real number (ℝ)

MISSING  SKEWED 

Distinct1276
Distinct (%)2.4%
Missing23270
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean3.4986658
Minimum0.01
Maximum183.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:57.461475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.37
Q12.16
median3.95
Q34.13
95-th percentile4.38
Maximum183.82
Range183.81
Interquartile range (IQR)1.97

Descriptive statistics

Standard deviation4.0550148
Coefficient of variation (CV)1.1590175
Kurtosis658.51609
Mean3.4986658
Median Absolute Deviation (MAD)0.25
Skewness20.976506
Sum185607.72
Variance16.443145
MonotonicityNot monotonic
2023-03-20T22:30:57.676371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.03 803
 
1.1%
4.05 799
 
1.0%
4.1 779
 
1.0%
4.04 776
 
1.0%
4.02 774
 
1.0%
4.07 756
 
1.0%
4.09 743
 
1.0%
4.11 735
 
1.0%
3.99 733
 
1.0%
4.06 726
 
1.0%
Other values (1266) 45427
59.5%
(Missing) 23270
30.5%
ValueCountFrequency (%)
0.01 75
0.1%
0.02 68
0.1%
0.03 60
0.1%
0.04 74
0.1%
0.05 58
0.1%
0.06 62
0.1%
0.07 57
0.1%
0.08 71
0.1%
0.09 69
0.1%
0.1 60
0.1%
ValueCountFrequency (%)
183.82 1
< 0.1%
180.8 1
< 0.1%
176.55 1
< 0.1%
166.41 1
< 0.1%
165.47 1
< 0.1%
160.77 1
< 0.1%
156.17 1
< 0.1%
150.08 1
< 0.1%
143.1 1
< 0.1%
141.92 1
< 0.1%

Benzene
Real number (ℝ)

MISSING  ZEROS 

Distinct969
Distinct (%)1.6%
Missing13945
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean0.73635693
Minimum0
Maximum26.11
Zeros35154
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:57.878371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.98
95-th percentile3.52
Maximum26.11
Range26.11
Interquartile range (IQR)0.98

Descriptive statistics

Standard deviation1.3715823
Coefficient of variation (CV)1.8626595
Kurtosis15.285108
Mean0.73635693
Median Absolute Deviation (MAD)0
Skewness3.1058675
Sum45931
Variance1.8812379
MonotonicityNot monotonic
2023-03-20T22:30:58.075306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35154
46.1%
0.29 154
 
0.2%
0.15 153
 
0.2%
0.23 150
 
0.2%
0.3 149
 
0.2%
0.25 148
 
0.2%
0.16 147
 
0.2%
0.22 145
 
0.2%
0.19 143
 
0.2%
0.49 142
 
0.2%
Other values (959) 25891
33.9%
(Missing) 13945
 
18.3%
ValueCountFrequency (%)
0 35154
46.1%
0.01 64
 
0.1%
0.02 61
 
0.1%
0.03 75
 
0.1%
0.04 85
 
0.1%
0.05 81
 
0.1%
0.06 101
 
0.1%
0.07 117
 
0.2%
0.08 100
 
0.1%
0.09 110
 
0.1%
ValueCountFrequency (%)
26.11 1
< 0.1%
21.6 1
< 0.1%
18.87 1
< 0.1%
17.12 1
< 0.1%
16.53 1
< 0.1%
15.06 1
< 0.1%
14.84 1
< 0.1%
14.71 1
< 0.1%
14.64 1
< 0.1%
13.93 1
< 0.1%

Toluene
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing76321
Missing (%)100.0%
Memory size1.2 MiB

Eth-Benzene
Real number (ℝ)

Distinct4257
Distinct (%)11.5%
Missing39409
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean11.107763
Minimum0.02
Maximum162.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:58.264398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.94
Q13.92
median8.41
Q315.17
95-th percentile30.51
Maximum162.62
Range162.6
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation10.116801
Coefficient of variation (CV)0.91078658
Kurtosis7.7574909
Mean11.107763
Median Absolute Deviation (MAD)5.2
Skewness2.0646215
Sum410009.74
Variance102.34967
MonotonicityNot monotonic
2023-03-20T22:30:58.444206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.84 45
 
0.1%
3.83 44
 
0.1%
1.47 42
 
0.1%
3.96 42
 
0.1%
1.75 40
 
0.1%
4.79 40
 
0.1%
1.28 39
 
0.1%
1.43 39
 
0.1%
4.25 38
 
< 0.1%
2.57 38
 
< 0.1%
Other values (4247) 36505
47.8%
(Missing) 39409
51.6%
ValueCountFrequency (%)
0.02 2
 
< 0.1%
0.03 3
 
< 0.1%
0.04 15
< 0.1%
0.05 21
< 0.1%
0.06 24
< 0.1%
0.07 19
< 0.1%
0.08 22
< 0.1%
0.09 19
< 0.1%
0.1 14
< 0.1%
0.11 16
< 0.1%
ValueCountFrequency (%)
162.62 1
< 0.1%
124.75 1
< 0.1%
111.33 1
< 0.1%
101.42 1
< 0.1%
99.32 1
< 0.1%
97.33 1
< 0.1%
94.12 1
< 0.1%
93.39 1
< 0.1%
91.42 1
< 0.1%
87.81 1
< 0.1%

MP-Xylene
Real number (ℝ)

Distinct7385
Distinct (%)23.5%
Missing44953
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean24.965661
Minimum0.03
Maximum291.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:58.708473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile2.06
Q18.34
median18.325
Q334.01
95-th percentile70.3795
Maximum291.2
Range291.17
Interquartile range (IQR)25.67

Descriptive statistics

Standard deviation23.434662
Coefficient of variation (CV)0.93867584
Kurtosis7.4400563
Mean24.965661
Median Absolute Deviation (MAD)11.645
Skewness2.1054014
Sum783122.84
Variance549.1834
MonotonicityNot monotonic
2023-03-20T22:30:58.916098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.96 29
 
< 0.1%
5.79 29
 
< 0.1%
2.27 27
 
< 0.1%
9.41 26
 
< 0.1%
5.3 25
 
< 0.1%
11.58 25
 
< 0.1%
3.53 24
 
< 0.1%
4.34 23
 
< 0.1%
14.01 22
 
< 0.1%
12.12 22
 
< 0.1%
Other values (7375) 31116
40.8%
(Missing) 44953
58.9%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.06 5
< 0.1%
0.07 2
 
< 0.1%
0.08 5
< 0.1%
0.09 8
< 0.1%
0.1 4
 
< 0.1%
0.11 11
< 0.1%
0.12 8
< 0.1%
0.13 4
 
< 0.1%
0.14 4
 
< 0.1%
ValueCountFrequency (%)
291.2 1
< 0.1%
263.77 1
< 0.1%
247.73 1
< 0.1%
242.57 1
< 0.1%
228.49 1
< 0.1%
225.23 1
< 0.1%
223.47 1
< 0.1%
218.3 1
< 0.1%
211.72 1
< 0.1%
205.61 1
< 0.1%

RH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6373
Distinct (%)11.4%
Missing20376
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean77.727891
Minimum14.48
Maximum99.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:59.122707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14.48
5-th percentile51.164
Q169.46
median79.87
Q387.75
95-th percentile97.82
Maximum99.77
Range85.29
Interquartile range (IQR)18.29

Descriptive statistics

Standard deviation13.990029
Coefficient of variation (CV)0.17998725
Kurtosis0.48375948
Mean77.727891
Median Absolute Deviation (MAD)8.96
Skewness-0.77021432
Sum4348486.9
Variance195.72092
MonotonicityNot monotonic
2023-03-20T22:30:59.517826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.76 559
 
0.7%
99.77 54
 
0.1%
99.75 32
 
< 0.1%
83.23 31
 
< 0.1%
82.76 30
 
< 0.1%
87.84 30
 
< 0.1%
84.73 30
 
< 0.1%
85.76 29
 
< 0.1%
84.08 29
 
< 0.1%
82.63 29
 
< 0.1%
Other values (6363) 55092
72.2%
(Missing) 20376
 
26.7%
ValueCountFrequency (%)
14.48 1
< 0.1%
15.59 1
< 0.1%
17.64 1
< 0.1%
19.11 1
< 0.1%
20.98 1
< 0.1%
21.09 1
< 0.1%
21.72 1
< 0.1%
21.73 1
< 0.1%
22.06 1
< 0.1%
22.19 1
< 0.1%
ValueCountFrequency (%)
99.77 54
 
0.1%
99.76 559
0.7%
99.75 32
 
< 0.1%
99.74 18
 
< 0.1%
99.73 11
 
< 0.1%
99.72 9
 
< 0.1%
99.71 11
 
< 0.1%
99.7 13
 
< 0.1%
99.69 11
 
< 0.1%
99.68 10
 
< 0.1%

WS
Real number (ℝ)

Distinct432
Distinct (%)0.8%
Missing20383
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean1.0875764
Minimum0.21
Maximum46.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:30:59.712402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.39
Q10.61
median1
Q31.39
95-th percentile2.2
Maximum46.35
Range46.14
Interquartile range (IQR)0.78

Descriptive statistics

Standard deviation0.65869219
Coefficient of variation (CV)0.60565141
Kurtosis761.5377
Mean1.0875764
Median Absolute Deviation (MAD)0.39
Skewness12.976651
Sum60836.85
Variance0.43387541
MonotonicityNot monotonic
2023-03-20T22:30:59.911198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49 591
 
0.8%
0.51 581
 
0.8%
0.44 568
 
0.7%
0.48 547
 
0.7%
0.47 537
 
0.7%
0.46 532
 
0.7%
0.5 529
 
0.7%
0.54 525
 
0.7%
0.53 513
 
0.7%
0.43 506
 
0.7%
Other values (422) 50509
66.2%
(Missing) 20383
26.7%
ValueCountFrequency (%)
0.21 2
 
< 0.1%
0.22 4
 
< 0.1%
0.23 13
 
< 0.1%
0.24 15
 
< 0.1%
0.25 20
 
< 0.1%
0.26 33
 
< 0.1%
0.27 49
 
0.1%
0.28 65
0.1%
0.29 76
0.1%
0.3 123
0.2%
ValueCountFrequency (%)
46.35 1
< 0.1%
43.58 1
< 0.1%
26.64 1
< 0.1%
19.93 1
< 0.1%
6.4 1
< 0.1%
6.1 1
< 0.1%
6.08 1
< 0.1%
5.96 1
< 0.1%
5.88 1
< 0.1%
5.84 1
< 0.1%

WD
Real number (ℝ)

Distinct17502
Distinct (%)31.3%
Missing20348
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean196.87825
Minimum6
Maximum327.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:31:00.111492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile116.586
Q1161.09
median204.58
Q3232.56
95-th percentile263.51
Maximum327.92
Range321.92
Interquartile range (IQR)71.47

Descriptive statistics

Standard deviation46.260301
Coefficient of variation (CV)0.23496908
Kurtosis-0.56198226
Mean196.87825
Median Absolute Deviation (MAD)33.6
Skewness-0.35964735
Sum11019866
Variance2140.0155
MonotonicityNot monotonic
2023-03-20T22:31:00.295392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
229.72 15
 
< 0.1%
227.48 15
 
< 0.1%
223.15 15
 
< 0.1%
233.08 15
 
< 0.1%
235.33 14
 
< 0.1%
230.77 14
 
< 0.1%
226.57 14
 
< 0.1%
231.41 14
 
< 0.1%
232.38 14
 
< 0.1%
230.51 14
 
< 0.1%
Other values (17492) 55829
73.2%
(Missing) 20348
 
26.7%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 8
< 0.1%
7.61 1
 
< 0.1%
9 2
 
< 0.1%
10 6
< 0.1%
11 3
 
< 0.1%
11.76 1
 
< 0.1%
12 2
 
< 0.1%
13 1
 
< 0.1%
13.07 1
 
< 0.1%
ValueCountFrequency (%)
327.92 1
< 0.1%
327.19 1
< 0.1%
315.4 1
< 0.1%
314.12 1
< 0.1%
313.45 1
< 0.1%
312.87 1
< 0.1%
311.61 1
< 0.1%
311.55 1
< 0.1%
311.11 1
< 0.1%
310.14 1
< 0.1%

BP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1857
Distinct (%)10.4%
Missing58514
Missing (%)76.7%
Infinite0
Infinite (%)0.0%
Mean1010.0248
Minimum997.07
Maximum1018.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:31:00.487120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum997.07
5-th percentile1002.773
Q11008.03
median1010.42
Q31012.53
95-th percentile1015.34
Maximum1018.72
Range21.65
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.7311968
Coefficient of variation (CV)0.0036941635
Kurtosis1.1378555
Mean1010.0248
Median Absolute Deviation (MAD)2.25
Skewness-0.83090548
Sum17985511
Variance13.921829
MonotonicityNot monotonic
2023-03-20T22:31:00.664070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011.57 36
 
< 0.1%
1011.42 34
 
< 0.1%
1011.7 31
 
< 0.1%
1010.09 31
 
< 0.1%
1009.73 31
 
< 0.1%
1010.5 31
 
< 0.1%
1010.96 31
 
< 0.1%
1011.8 30
 
< 0.1%
1011.51 30
 
< 0.1%
1009.88 30
 
< 0.1%
Other values (1847) 17492
 
22.9%
(Missing) 58514
76.7%
ValueCountFrequency (%)
997.07 1
< 0.1%
997.12 1
< 0.1%
997.17 1
< 0.1%
997.18 1
< 0.1%
997.2 1
< 0.1%
997.22 1
< 0.1%
997.3 1
< 0.1%
997.31 1
< 0.1%
997.32 1
< 0.1%
997.33 2
< 0.1%
ValueCountFrequency (%)
1018.72 1
 
< 0.1%
1018.69 1
 
< 0.1%
1018.68 2
< 0.1%
1018.64 3
< 0.1%
1018.63 1
 
< 0.1%
1018.62 1
 
< 0.1%
1018.61 1
 
< 0.1%
1018.57 1
 
< 0.1%
1018.56 1
 
< 0.1%
1018.55 1
 
< 0.1%

AT
Real number (ℝ)

Distinct1871
Distinct (%)3.3%
Missing20455
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean26.647006
Minimum14.89
Maximum37.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2023-03-20T22:31:00.854102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum14.89
5-th percentile21.1
Q124.7
median26.73
Q328.75
95-th percentile31.75
Maximum37.67
Range22.78
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation3.1415448
Coefficient of variation (CV)0.11789485
Kurtosis0.050479657
Mean26.647006
Median Absolute Deviation (MAD)2.02
Skewness-0.23315962
Sum1488661.6
Variance9.8693037
MonotonicityNot monotonic
2023-03-20T22:31:01.042326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.79 103
 
0.1%
27.12 102
 
0.1%
26.98 100
 
0.1%
26.73 97
 
0.1%
27.16 96
 
0.1%
26.86 93
 
0.1%
26.82 93
 
0.1%
26.8 93
 
0.1%
26.69 93
 
0.1%
27.14 93
 
0.1%
Other values (1861) 54903
71.9%
(Missing) 20455
 
26.8%
ValueCountFrequency (%)
14.89 1
< 0.1%
15.01 1
< 0.1%
15.09 1
< 0.1%
15.11 1
< 0.1%
15.13 1
< 0.1%
15.16 1
< 0.1%
15.17 1
< 0.1%
15.24 1
< 0.1%
15.29 1
< 0.1%
15.3 1
< 0.1%
ValueCountFrequency (%)
37.67 1
< 0.1%
37.61 1
< 0.1%
37.6 1
< 0.1%
37.4 1
< 0.1%
37.3 1
< 0.1%
37.22 1
< 0.1%
37.1 1
< 0.1%
37.04 1
< 0.1%
37 1
< 0.1%
36.9 1
< 0.1%

TOT-RF
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
76321 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters228963
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 76321
100.0%

Length

2023-03-20T22:31:01.209017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-20T22:31:01.383582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 76321
100.0%

Most occurring characters

ValueCountFrequency (%)
0 152642
66.7%
. 76321
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152642
66.7%
Other Punctuation 76321
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 152642
100.0%
Other Punctuation
ValueCountFrequency (%)
. 76321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 228963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 152642
66.7%
. 76321
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152642
66.7%
. 76321
33.3%

Interactions

2023-03-20T22:30:48.112304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.146657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.899676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.635509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.214482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.734332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.482492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.128358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.982594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.599330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.272861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.017421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.699440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.368177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.115224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.664255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.373072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:48.262640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.342724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.057087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.801550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.367187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.033585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.638352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.298271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.141446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.766390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.426281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.182428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.854805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.520767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.270833image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.820809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.515190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:48.407897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.492317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.204144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.952341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.516342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.190814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.789174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.459988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.292046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.921311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.577747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.335356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.008003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.668852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.420345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.965952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.651454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:48.553354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.640990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.472232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.096194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.660210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.342928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.940570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.608705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.440888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.073034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.730598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.487334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.163141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.814614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.566487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.110893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.785274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:48.691884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.794474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.627205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.241207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.797396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.495834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.111256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.760676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.584439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.225527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.875891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.643319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.309155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.964237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.708115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.254525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.917185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:48.848891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:05.959511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.784456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.394343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.940326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.641149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.258706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:21.908655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.740357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.387013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.027867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.800193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.468773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.109094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:40.859029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.399132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.068250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.007262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:06.122766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:08.933132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.547082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.097537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.792070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.414590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.067377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:24.899706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.538811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.176648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:32.953225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.618762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.258990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.011293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.550997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.258739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.170086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:06.334209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.107831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.701374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.253461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:16.946340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.572766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.228050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.067318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.703320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.346597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.114836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.800384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.416555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.168147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.712420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.395240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.327429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:06.505254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.266199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:11.858177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.407372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.103212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.727329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.418179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.224113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:27.867316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.500745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.269693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:35.971274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.580673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.324323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:43.984271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.545565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.490276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:06.685181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.433637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.021088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.566304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.268362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:19.890700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.591141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.392516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.037454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.663130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.435919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.145338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.741018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.492289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.163508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.691521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.632351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:06.832192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.584849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.167346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.717192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.419245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.041308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.744466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.540190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.194188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.808392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.596279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.293299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:38.882108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.632495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.313286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.831278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.795589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.001183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.749234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.331399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:14.881970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.584331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.254914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:22.919381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.689171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.352183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:30.972085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.778768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.459498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.238504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.785893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.485328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:46.989992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:49.956556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.156658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:09.905099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.489214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.031177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.751461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.406345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.090530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:25.863137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.524061image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:31.283789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:33.938920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.618256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.404132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:41.942136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.639975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:47.151329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:50.202619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.304104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.050384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.633338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.171358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:17.904100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.551339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.246614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.015309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.673822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:31.432629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.098267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.767116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.540791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.077292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.785191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:47.313007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:50.362356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.452434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.199150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.780335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.313522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.054320image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.693353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.399452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.160455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.824310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:31.587875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.249534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:36.920501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.681009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.210174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:44.949390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:47.453558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:50.539280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.608369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.341430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:12.928429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.454693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.198080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.843414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.546270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.311665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:28.977556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:31.738314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.406613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.075341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.822852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.352135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.096673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:47.754701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:50.682266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:07.751546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:10.485414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:13.069841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:15.594583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:18.340591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:20.977356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:23.833514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:26.451484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:29.126307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:31.874092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:34.544160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:37.216341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:39.971151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:42.506373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:45.238421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-20T22:30:47.964269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-20T22:31:01.511466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
PM2.5PM10NONO2NOxNH3SO2COOzoneBenzeneEth-BenzeneMP-XyleneRHWSWDBPAT
PM2.51.0000.902-0.1460.5410.0300.3230.2060.223-0.159-0.132-0.074-0.096-0.495-0.226-0.1970.637-0.196
PM100.9021.000-0.0750.4740.0130.3740.2610.208-0.066-0.185-0.082-0.091-0.525-0.179-0.1280.626-0.120
NO-0.146-0.0751.000-0.3050.8070.0760.0700.1710.211-0.063-0.011-0.0070.138-0.210-0.070-0.2470.058
NO20.5410.474-0.3051.0000.1570.3410.1140.233-0.188-0.106-0.096-0.112-0.4140.039-0.0070.274-0.157
NOx0.0300.0130.8070.1571.000-0.0390.0640.1730.0450.030-0.037-0.044-0.000-0.1170.005-0.1510.028
NH30.3230.3740.0760.341-0.0391.0000.4470.3020.069-0.269-0.090-0.098-0.202-0.064-0.0820.096-0.068
SO20.2060.2610.0700.1140.0640.4471.0000.1680.226-0.0350.0320.034-0.1560.041-0.0010.287-0.002
CO0.2230.2080.1710.2330.1730.3020.1681.0000.174-0.228-0.083-0.098-0.059-0.056-0.1010.490-0.116
Ozone-0.159-0.0660.211-0.1880.0450.0690.2260.1741.000-0.168-0.069-0.0630.1100.0250.022-0.3430.074
Benzene-0.132-0.185-0.063-0.1060.030-0.269-0.035-0.228-0.1681.0000.1750.1650.0810.0630.085-0.1560.098
Eth-Benzene-0.074-0.082-0.011-0.096-0.037-0.0900.032-0.083-0.0690.1751.0000.1010.0300.0480.040-0.0150.046
MP-Xylene-0.096-0.091-0.007-0.112-0.044-0.0980.034-0.098-0.0630.1650.1011.0000.0290.0650.052-0.0260.053
RH-0.495-0.5250.138-0.414-0.000-0.202-0.156-0.0590.1100.0810.0300.0291.000-0.191-0.090-0.547-0.385
WS-0.226-0.179-0.2100.039-0.117-0.0640.041-0.0560.0250.0630.0480.065-0.1911.0000.428-0.1100.455
WD-0.197-0.128-0.070-0.0070.005-0.082-0.001-0.1010.0220.0850.0400.052-0.0900.4281.000-0.2180.482
BP0.6370.626-0.2470.274-0.1510.0960.2870.490-0.343-0.156-0.015-0.026-0.547-0.110-0.2181.000-0.191
AT-0.196-0.1200.058-0.1570.028-0.068-0.002-0.1160.0740.0980.0460.053-0.3850.4550.482-0.1911.000

Missing values

2023-03-20T22:30:50.989606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-20T22:30:51.631766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-20T22:30:52.441229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DatePM2.5PM10NONO2NOxNH3SO2COOzoneBenzeneTolueneEth-BenzeneMP-XyleneRHWSWDBPATTOT-RF
02021-01-01 00:15:00153.0208.317.6034.8242.4220.5118.200.900.870.00NaN6.8431.0186.380.56230.78NaN23.670.0
12021-01-01 00:30:00153.0212.007.9434.1842.1219.8218.010.890.800.06NaN39.883.7286.830.69255.87NaN23.650.0
22021-01-01 00:45:00153.0212.007.7530.3738.1219.8818.210.890.942.75NaN33.42109.1486.860.90259.70NaN23.600.0
32021-01-01 01:00:00NaN212.007.6328.8136.4520.1018.160.880.950.00NaN0.4236.8886.850.58257.98NaN23.500.0
42021-01-01 01:15:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
52021-01-01 01:30:00NaN231.007.9428.8236.7519.4918.170.881.240.93NaN30.5327.7987.160.46219.19NaN23.480.0
62021-01-01 01:45:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
72021-01-01 02:00:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
82021-01-01 02:15:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
92021-01-01 02:30:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
DatePM2.5PM10NONO2NOxNH3SO2COOzoneBenzeneTolueneEth-BenzeneMP-XyleneRHWSWDBPATTOT-RF
763112023-06-03 22:00:0083.00152.0013.1143.3156.43NaN0.761.670.520.0NaN17.99NaN49.001.3891.101014.2726.830.0
763122023-06-03 22:15:0091.31151.1116.6950.8067.53NaN0.771.651.670.0NaN10.23NaN48.930.9891.431014.3226.660.0
763132023-06-03 22:30:0094.00144.0014.2645.8660.11NaN0.941.640.800.0NaNNaNNaN47.811.08112.951014.2426.740.0
763142023-06-03 22:45:0094.00144.0010.8239.3350.14NaN0.601.651.580.0NaN13.808.7146.891.81113.511014.1826.940.0
763152023-06-03 23:00:0094.00144.007.6334.7842.38NaN0.701.651.620.0NaN7.9942.6946.841.78110.211014.1626.910.0
763162023-06-03 23:15:0081.73143.339.7134.3344.03NaN0.601.651.400.0NaN2.2521.1846.411.53136.121014.1327.000.0
763172023-06-03 23:30:0078.00138.0010.5737.7448.30NaN0.451.640.100.0NaN1.24NaN45.891.23114.841014.1227.120.0
763182023-06-03 23:45:0078.00138.0010.7835.4846.25NaN0.451.642.100.0NaN1.60NaN45.880.93140.381014.2427.140.0
763192023-07-03 00:00:0078.00138.0012.3632.0044.37NaN0.611.651.480.0NaN0.90NaN46.370.88130.711014.2426.980.0
763202023-07-03 00:00:0075.77NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0